From Partial Exchangeability to Predictive Probability: A Bayesian Perspective on Classification

• URL: http://arxiv.org/abs/2508.16716v1
• Date: Fri, 22 Aug 2025 17:51:24 GMT
• Title: From Partial Exchangeability to Predictive Probability: A Bayesian Perspective on Classification
• Authors: Marcio Alves Diniz,
• Abstract summary: We propose a novel Bayesian nonparametric classification model that combines a Gaussian process prior for the latent function with a Dirichlet process prior for the link function.<n>We demonstrate the method performance using simulated data where it outperforms standard logistic regression.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel Bayesian nonparametric classification model that combines a Gaussian process prior for the latent function with a Dirichlet process prior for the link function, extending the interpretative framework of de Finetti representation theorem and the construction of random distribution functions made by Ferguson (1973). This approach allows for flexible uncertainty modeling in both the latent score and the mapping to probabilities. We demonstrate the method performance using simulated data where it outperforms standard logistic regression.

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